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Creating a “Fairness-Aware” Financial Crime Culture with Responsible AI-based Systems
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Global Banking & Finance Review®
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By Danny Butvinik, Chief Data Scientist, NICE Actimize
By Danny Butvinik, Chief Data Scientist, NICE Actimize
Danny Butvinik, Chief Data Scientist, NICE Actimize
Some of the newest, advanced technologies that are being launched often have their own specific issues that must be considered during adoption stages to successfully fight fraudsters without regulatory repercussions. In fraud detection, model fairness and data bias can occur when a system is more heavily weighted or lacking representation of certain groups or categories of data. In theory, a predictive model could erroneously associate last names from other cultures with fraudulent accounts, or falsely decrease risk within population segments for certain type of financial activities.
Biased AI systems can represent a serious threat when reputations may be affected and occurs when available data is not representative of the population or phenomenon of exploration. This data does not include variables that properly capture the phenomenon we want to predict. Or alternatively the data could include content produced by humans which may contain bias against groups of people, inherited by cultural and personal experiences, leading to distortions when making decisions. While at first data might seem objective, it is still collected and analyzed by humans, and can therefore be biased.
While there isn’t a silver bullet when it comes to remediating the dangers of discrimination and unfairness in AI systems or permanent fixes to the problem of fairness and bias mitigation in architecting machine learning model and use, these issues must be considered for both societal and business reasons.
Addressing bias in AI-based systems is not only the right thing, but the smart thing for business — and the stakes for business leaders are high. Biased AI systems can lead financial institutions down the wrong path by allocating opportunities, resources, information, or quality of service unfairly. They even have the potential to infringe on civil liberties, pose a detriment to the safety of individuals, or impact a person’s well-being if perceived as disparaging or offensive.
The Power and Risks of AI Bias
It’s important for enterprises to understand the power and risks of AI bias. Although often unknown by the institution, a biased AI-based system could be using detrimental models or data that exposes race or gender bias into a lending decision. Information such as names and gender could be proxies for categorizing and identifying applicants in illegal ways. Even if the bias is unintentional, it still puts the organization at risk by not complying with regulatory requirements and could lead to certain groups of people being unfairly denied loans or lines of credit.
Currently, organizations don’t have the pieces in place to successfully mitigate bias in AI systems. But with AI increasingly being deployed across businesses to inform decisions, it’s vital that organizations strive to reduce bias, not just for moral reasons, but to comply with regulatory requirements and build revenue.
“Fairness-Aware” Culture and Implementation
Solutions that are focused on fairness-aware design and implementation will have the most beneficial outcomes. Providers should have an analytical culture that considers responsible data acquisition, handling, and management as necessary components of algorithmic fairness, because if the results of an AI project are generated by biased, compromised, or skewed datasets, affected parties will not adequately be protected from discriminatory harm.
There are numerous elements of data fairness that data science teams must keep in mind. First depending on the context, either underrepresentation or overrepresentation of disadvantaged or legally protected groups in the data sample may lead to the systematic disadvantaging the vulnerable parties in the outcomes of the trained model. To avoid such kinds of sampling bias, domain expertise will be crucial to assess the fit between the data collected or acquired and the underlying population to be modelled. Technical team members should offer means of remediation to correct for representational flaws in the sampling.
It is also important to understand if the data collected is sufficient for the intended purpose of the project. Insufficient datasets may not equitably reflect the qualities that should be weighed to produce a justified outcome that is consistent with the desired purpose of the AI system. Accordingly, members of the project team with technical and policy competencies should collaborate to determine if the data quantity is sufficient and fit-for-purpose.
Source Integrity and Measurement Accuracy
Effective bias mitigation starts at the very beginning of data extraction and collection processes. Both the sources and tools of measurement may introduce discriminatory factors into a dataset. To secure discriminatory non-harm, the data sample must have an optimal source integrity. This involves securing or confirming that the data gathering processes involved suitable, reliable, and impartial sources of measurement and robust methods of collection.
If the datasets include outdated data, then changes in the underlying data distribution may adversely affect the generalizability of the trained model. Provided these distributional drifts reflect changing social relationship or group dynamics, this loss of accuracy regarding actual characteristics of the underlying population may introduce bias into the AI system. In preventing discriminatory outcomes, timeliness, and recency of all elements of the dataset should be scrutinized.
Relevance, Appropriateness and Domain Knowledge
The understanding and use of the most appropriate sources and types of data are crucial for building a robust and unbiased AI system. Solid domain knowledge of the underlying population distribution, and of the predictive goal of the project, is instrumental for selecting optimally relevant measurement inputs that contribute to the reasonable resolution of the defined solution. Domain experts should collaborate closely with data science teams to assist in determining optimally appropriate categories and sources of measurement.
While AI-based systems assist in decision-making automation processes and deliver cost savings, financial institutions considering AI as a solution must be vigilant to ensure biased decisions are not taking place. Compliance leaders should be in lockstep with their data science team to confirm that AI capabilities are responsible, effective, and free of bias. Having a strategy that champions responsible AI is the right thing to do, and it may also provide a path to compliance with future AI regulations.
AI bias refers to the systematic and unfair discrimination that can occur in artificial intelligence systems, often due to biased training data or algorithms that do not represent all groups fairly.
Financial crime encompasses a range of illegal activities that involve deceit or fraud for financial gain, including money laundering, fraud, and embezzlement.
Compliance in finance refers to the adherence to laws, regulations, and guidelines governing financial institutions and their operations to prevent fraud and protect consumers.
Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle, ensuring that it remains unaltered and trustworthy.
Risk management is the process of identifying, assessing, and prioritizing risks followed by coordinated efforts to minimize, monitor, and control the probability or impact of unfortunate events.
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